Determining general causation from processing scientific articles

ABSTRACT

Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/135,436, entitled “Determining General Causation From ProcessingScientific Articles” (now U.S. Patent. Pub. No. 2015/0178628) filed Dec.19, 2013, which is a continuation of U.S. patent application Ser. No.15/217,820, entitled “Discover and Scoring of Causal Assertions inScientific Publications” (now U.S. Patent. Pub. No. 2016/0328652) filedJul. 22, 2016, each of which is hereby incorporated by reference in itsentirety.

FIELD OF THE DISCLOSURE

This relates generally to methods of determining causation of an outcomeby an agent.

SUMMARY

Examples of the disclosure are directed toward generating a causationscore with respect to an agent and an outcome, and projecting a futurecausation score distribution. For example, a causation score may bedetermined with respect to a hypothesis that a given agent causes agiven outcome, and the score may indicate the acceptance of thathypothesis in the scientific community, as described by scientificliterature. A future causation score distribution, then, may indicate aprobability distribution over possible future causation scores, therebypredicting the scientific acceptance of the hypothesis at some specificdate in the future. An agent may include any hypothesized cause of anoutcome, including a chemical, a material, a process, a businesspractice, and/or a behavior, among numerous other possibilities.

In some examples, a causation score may be determined based on a corpusof scientific publications, such as a database of articles and/orabstracts, or metadata corresponding to individual scientificpublications. For example, each abstract or article may be annotatedwith metadata, and the causation score may be determined based on someor all of the set of metadata across the corpus. A future causationscore distribution can be projected by first generating one or morefuture publication datasets, and then determining causation scores foreach of the one or more future publication datasets.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate an exemplary method of determining acausation score according to examples of the disclosure.

FIG. 2 illustrates an exemplary method of determining a causation scoreaccording to examples of the disclosure.

FIG. 3 illustrates an exemplary projected causation score distributionaccording to examples of the disclosure.

FIG. 4 illustrates another exemplary projected causation scoredistribution according to examples of the disclosure.

FIG. 5 illustrates an exemplary method for projecting a causation scoredistribution according to examples of the disclosure.

FIG. 6 illustrates an exemplary sample future publication dataset from aweighted mixture of publication distributions.

FIG. 7 illustrates an exemplary system for determining a causation scoreaccording to examples of the disclosure.

DETAILED DESCRIPTION

In the following description of embodiments, reference is made to theaccompanying drawings which form a part hereof, and in which it is shownby way of illustration specific embodiments which can be practiced. Itis to be understood that other embodiments can be used and structuralchanges can be made without departing from the scope of the disclosedembodiments.

Examples of the disclosure are directed toward generating a causationscore with respect to an agent and an outcome, and projecting a futurecausation score distribution. For example, a causation score may bedetermined with respect to a hypothesis that a given agent causes agiven outcome, and the score may indicate the acceptance of thathypothesis in the scientific community, as described by scientificliterature. A future causation score distribution, then, may indicate aprobability distribution over possible future causation scores, therebypredicting the scientific acceptance of the hypothesis at some specificdate in the future. An agent may include any hypothesized cause of anoutcome, including a chemical, a material, a process, a businesspractice, and/or a behavior, among numerous other possibilities.

In some examples, a causation score may be determined based on a corpusof scientific publications, such as a database of articles and/orabstracts, or metadata corresponding to individual scientificpublications. For example, each abstract or article may be annotatedwith metadata, and the causation score may be determined based on someor all of the set of metadata across the corpus. A future causationscore distribution can be projected by first generating one or morefuture publication datasets, and then determining causation scores foreach of the one or more future publication datasets.

Although examples of the disclosure may be discussed with reference todetermining scientific acceptance of a causation hypothesis, the methodsdisclosed are not so limited and may apply to determining a causationscore in general. Additionally, although examples may be described withreference to biomedical science literature, the examples are not solimited and may apply to natural science literature in general. Further,the equations provided herein are merely examples to illustrate thecalculation of various scores, but the examples are not so limited andalternative and additional formulations are contemplated.

As discussed above, a causation score may be determined based onmetadata of an annotated corpus of scientific publications. Theannotations may be associated with a particular agent and a particularoutcome. For example, a corpus of scientific publications may beannotated with respect to the agent bisphenol A (BPA) and the outcomebreast cancer, and the metadata would be associated with thatagent/outcome pair. Such metadata can include directionality data,evidence data, proximity data, and/or magnitude data, among otherpossibilities.

Directionality data can indicate whether an article supports or rejectsa hypothesis that the agent causes the outcome. For example, a 1 canindicate that the article supports the hypothesis, a −1 can indicatethat the article rejects the hypothesis, and a 0 can indicate that thearticle is uncertain on the hypothesis.

Evidence data can indicate the evidence level of an article, that is,how well the methodology of the article can demonstrate a causalrelationship. For example, a randomized, controlled trial candemonstrate a causal relationship well. Such an article may have ahigher value than an uncontrolled observational study, which may notdemonstrate a causal relationship as well. Evidence level may beannotated based on a plurality of categories of study design, and eachcategory may be associated with a value on the interval [0,1],reflective of the category's relative importance in informing the causalhypothesis for a specified agent and outcome.

Proximity data can indicate whether the evidence provided in the articleis direct evidence or indirect evidence that an agent causes an outcomein a target population. In some examples, this may include a measure ofhow close the model used in the article is to the target population. Forexample, if the target population is humans, the hypothesis of interestis whether the agent causes the outcome in humans. In such a case, ananimal study would have a lower proximity value than a human study,because the humans in the study are more similar biologically to thetarget population and thus human evidence is more direct than animalevidence. In some examples, proximity data may comprise a simplecategorization of each study as either human, animal, or in vitro; insome examples, the proximity data may comprise a simple categorizationof each study as either indirect evidence or direct evidence. Theproximity data may only include articles/abstracts that are relevant tothe causal hypothesis for the specified agent and outcome.

Magnitude data can quantify the strength of the association between anagent and an outcome as observed in an article or abstract. For example,magnitude data can include odds ratios, statistical significance, riskratios, and/or standardized mortality ratios, among other possibilities.

The causation score may be further determined based on data that is notspecific to an agent/outcome pair. For example, the causation score maybe determined based on the quality of the journals in which the relevantliterature was published. This can be determined on the basis of thejournal, the author(s) of the article, the lab which conducted the studydescribed in the article, and/or the corporation that funded the study,among other possibilities. Literature impact data (also referred to asimpact factors) can be calculated, or in some examples literature impactdata may be obtained from a database of such information.

FIGS. 1A and 1B illustrate an exemplary method of determining acausation score according to examples of the disclosure. A literaturemagnetism score (100) may be calculated based on directionality data,evidence data, and/or impact factors, among other possibilities. Aliterature magnetism score may indicate an aggregate “direction” of therelevant scientific literature with respect to causation for a givenagent/outcome hypothesis. In some examples, the literature magnetismscore may be calculated based on the following equation:LM _(raw)=Σ_(i)√{square root over (IF _(i))}·EL _(i) ·d _(i)  (1)where, for each article or abstract i, IF may be its journal impactfactor, EL may be its evidence level, and d may be its directionality.LA_(raw) may be unbounded, with positive scores reflecting overallsupport for causation and negative scores reflecting a lack of support.The magnetism score may be constrained to the interval [−1,1] using ascaled sigmoidal squashing function, such as hyperbolic tangent. In someexamples, the following equation may be used:LM=tan h(αLM _(raw))  (2)The constant α may be a tuning parameter used to set the active range ofthe magnetism score, that is, over what range of scores will adding morepublications continue to affect the final score. In some examples, α maybe equal to 0.2. Interpreting d_(i) as a two-state choice parameter, amodeling analogy can be drawn to mean field theory and the mean fieldenergy of scientific consensus can be calculated. The effect of thisanalogy is to apply a hyperbolic tangent function to the raw literaturemagnetism score as illustrated in equation 2. Although examples aredescribed with respect to a literature magnetism score, a magnetismscore may take into account other evidence supporting or rejecting acausation hypothesis and, in fact, may be based on no scientificliterature in some examples. In some examples, a magnetism score may befurther based on one or more other data sets, such as magnitude data.

A proximity score (102) may be determined based on at least proximitydata. The proximity score can indicate the directness of the aggregateevidence in the scientific literature, as discussed above. In someexamples, the proximity score may be calculated based on the followingequation:

$\begin{matrix}{{P = \frac{1}{1 + e^{- {\beta{({x - 0.5})}}}}},{{{where}\mspace{14mu} x} = \frac{{human} + {{animal}/2}}{{human} + {animal} + {{in}\mspace{14mu}{{vitro}/4}}}}} & (3)\end{matrix}$The variables human, animal, and in vitro may indicate the total numberof articles/abstracts categorized in the proximity data as human,animal, and in vitro, respectively. The constant β may establish thesteepness of a transition zone and the width of a “flat” area of P whenx is near 0 or 1. In some examples, β may be equal to 15. In thisexample, a literature composed entirely of human studies would receive aproximity score of 1.0; whereas one with all animal studies wouldreceive a score of 0.5, and literatures absent human studies would bebounded at 0.5 or below. In some examples, a proximity score may becalculated based on categories other than human, animal, and invitro—for example, a proximity score may be calculated based on directevidence and indirect evidence categories, or the like.

A raw causation score GC_(raw) (104) may be calculated based on themagnetism score and the proximity score. In some examples, the rawcausation score may be calculated as the simple product of the magnetismscore and the proximity score. In some examples, the raw causation scoremay be calculated as the product LM^(a)·P^(b), where a and b areconstant parameters. In some examples, the raw causation score may be anintermediate result further modified as described with respect to FIG.1B. However, in other examples, the raw causation score may bedetermined to be the causation score for the agent/outcome hypothesisand accepted as a final result.

In FIG. 1B, a magnitude score (106) may be computed based on magnitudedata and literature impact data. The magnitude score may indicateaggregate effect size, conditional on study quality and statisticalsignificance. In some examples, the magnitude score may be calculatedbased on the following equation:

$\begin{matrix}{{M = {\tanh\left( \frac{x - 2}{\sqrt{2}} \right)}},{{{where}\mspace{14mu} x} = \frac{\sum\limits_{i}{\sqrt{{IF}_{i} \cdot b_{i}} \cdot {OR}_{i}}}{\sum\limits_{i}\sqrt{{IF}_{i} \cdot b_{i}}}}} & (4)\end{matrix}$where, for each article or abstract i, IF may be its journal impactfactor, OR may be its odds ratio, and b may indicate statisticalsignificance of the odds ratio (for example, b may be equal to 1 if theOR_(i) is statistically significant or 0.25 if non-significant).

A causation score GC_(mag) (108) may be calculated based on a rawcausation score GC_(raw) (104) moderated by the calculated magnitudescore M (106). For example, GC_(mag) may be calculated according to thefollowing conditions:

-   -   For positive GC_(raw)/positive M,        GC _(mag) =GC _(raw) +M(1−GC _(raw))  (5)    -   For positive GC_(raw)/negative M,        GC _(mag) =GC _(raw)(1+M)  (6)    -   For negative GC_(raw)/positive M,        GC _(mag) =GC _(raw)(1−M)  (7)    -   For negative GC_(raw)/negative M,        GC _(mag) =GC _(raw) +M(1+GC _(raw))  (8)

Finally, a coherence score may be computed based on directionality dataand/or proximity data, among other possibilities. For example, countdata may be tabulated to obtain, for each proximity category, the numberof positive studies and the number of negative studies (in someexamples, additional categories of directionality may be used). Then,test statistics (e.g., chi-squared) may be calculated based on the countdata to determine whether the ratio of positive to negative studies isstatistically different across the proximity categories. The test mayyield a chi-squared statistic corresponding to a p-value, and thecoherence score may be calculated by the following equation, among otherpossibilities:C=tan h(kp+tan⁻¹ m)  (9)where p may be the p-value calculated as described above, and k and mmay be parameters determining the steepness of the function and itsoffset. The coherence score may then be combined with themagnitude-adjusted causation score GC_(mag) to compute a causation scoreGC (112). For example, the magnitude-adjusted causation score may beweighted by the coherence score, although other combinations arepossible.

FIG. 2 illustrates an exemplary method of determining a causation scoreassociated with an agent and an outcome, according to examples of thedisclosure. Data associated with an agent and an outcome may beobtained, the data including directionality data, evidence data, andproximity data (201). As discussed above, such data may be stored asmetadata associated with a plurality of articles and/or abstracts. Amagnetism score (e.g., literature magnetism score) may be determinedbased on directionality data and evidence data (203). A magnetism scoremay be a literature magnetism score, and the score may be calculatedbased on directionality data, evidence data, and/or literature impactdata (e.g., impact factors), among other possibilities, as shown inequations 1 and 2. A proximity score may be determined based onproximity data (205), as shown in one example in equation 3. Themagnetism score may be weighted based on the proximity score (207), andthe causation score may be based on the weighted magnetism score (209).For example, the weighted magnetism score may be taken as the causationscore. In some examples, the weighted magnetism may be further combinedwith magnitude data, a magnitude score, and/or a coherence score, amongother possibilities, in order to determine the causation score.

Each of FIGS. 3 and 4 illustrate an exemplary projected causation scoredistribution according to examples of the disclosure. The line in eachgraph illustrates a determined causation score as a function of time,including both causation scores for years passed and projected causationscores for years in the future. The levels of shading around the line ineach graph illustrate the projected causation score distribution, witheach level representing deviation from the median of the distribution.FIGS. 1A, 1B, and 2 and accompanying text describe methods of computingcausation scores for the present and the past, and methods of projectingfuture causation score distributions are described with respect to FIG.5.

The causation score model discussed above can be extended by generatingsynthetic publication data for a specified time in the future and thenanalyzing the synthetic data using the causation score model discussedwith respect to FIGS. 1A, 1B, and 2. A Monte Carlo simulation method canbe employed to compute a causation score distribution based on multiplegenerated future publication datasets and corresponding causationscores. FIG. 5 illustrates an exemplary method for projecting acausation score distribution according to examples of the disclosure.

A plurality of distributions can be determined based on a currentpublication dataset (500). The current publication dataset can be slicedin a number of different ways to yield different publicationdistributions from which the future publication datasets can begenerated—that is, each distribution may be a subset of the currentpublication dataset, and the distributions may overlap, in part. Theplurality of distributions may include, among other possibilities: adistribution limited to publications relevant to the agent of interest,a distribution limited to publications relevant to the outcome ofinterest, a distribution limited to publications relevant to theagent/outcome pair of interest, and/or a distribution including everypublication, whether relevant or irrelevant. Each distribution may betime limited, for example, to the last five years or some other timethreshold. In some examples, an additional distribution may be limitedto publications from the n years after the causation score for theagent/outcome pair crossed a causation score threshold x, where n and xare parameters that can be set based on the hypothesis.

A plurality of future publication datasets may be generated from aweighted mixture of the plurality of distributions (502), and acausation score distribution may be determined based on the plurality offuture publication datasets (504). FIG. 6 illustrates generation of anexemplary sample future publication dataset from a weighted mixture ofpublication distributions. In FIG. 6, the current publication datasethas been sliced into three different distributions, Distribution 1,Distribution 2, and Distribution 3, each having its own weight in theweighted mixture. The weighted mixture may be sampled c times, where cis a simulated publication count, the calculation of which is discussedbelow. When the weighted mixture is sampled, each new “publication” maysimply be a copy of previously existing metadata from some article inone of the distributions, chosen randomly according to the weightedmixture model. The c samples from the weighted mixture make up a futurepublication dataset. In a Monte Carlo simulation, this process may berepeated a number of times to obtain a plurality of future publicationdatasets.

For example, in the Monte Carlo simulation, 1000 future publicationdatasets may be sampled from the weighted mixture of the plurality ofdistributions. Then, each of the 1000 future publication datasets may beanalyzed by the methods described with respect to FIGS. 1A, 1B, and 2 toobtain 1000 future causation scores, each corresponding to one of thefuture publication datasets. The 1000 future causation scores can beaggregated and analyzed to determine the causation score distribution.For example, a median future causation score can be determined, and oneor more confidence intervals can be determined based on the futurecausation scores. Although this example is discussed with reference to a1000-sample Monte Carlo simulation, methods described herein can beapplied to any number of samples in a Monte Carlo simulation.

The number of simulated publications in a future publication dataset canbe determined by predicting a future publication count. The annualpublication rate for a given body of literature can be approximated as arandom walk from the short term average publication rate. For example,if the current year is n and we wish to simulate the publication countfor the following year n+1, the history of publication counts from year1 to yearn can be analyzed to calculate the exponential moving average pand the variance σ². For year n+1, a number of samples (e.g., 3) can betaken from the distribution N(μ, σ²), and the average of those samplescan be used as the number of simulated publications in the year n+1. Ifmore than one year is being simulated, this projected count can be addedto the existing publication count stream, and the process can berepeated. In this way, publication counts can be simulated arbitrarilyfar into the future. Further, the above-described method of generatingfuture publication datasets can be recursed on a future publicationdataset to produce an additional future publication dataset for afollowing year, allowing future publication datasets and causation scoredistributions to be generated arbitrarily far into the future.

FIG. 7 illustrates an exemplary system 700 for determining a causationscore and projecting a causation score distribution. The system 700 caninclude a CPU 704, storage 702, memory 706, and display 708. The CPU 704can perform the methods illustrated in and described with reference toFIGS. 1A-6. Additionally, the storage 702 can store data andinstructions for performing the methods illustrated and described withreference to FIGS. 1A-6. The storage can be any non-transitory computerreadable storage medium, such as a solid-state drive or a hard diskdrive, among other possibilities. Visualizations of the data, such asthose illustrated in FIGS. 3 and 4 may be displayed on the display 708.

The system 700 can communicate with one or more remote users 712, 714,and 716 over a wired or wireless network 710, such as a local areanetwork, wide-area network, or internet, among other possibilities. Thesteps of the methods disclosed herein may be performed on a singlesystem 700 or on several systems including the remote users 712, 714,and 716.

Although the disclosed embodiments have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosed embodiments as defined by theappended claims.

What is claimed is:
 1. A computer-implemented method of displaying, on adisplay device, a general causation visualization for an agent and anoutcome, the method comprising: processing a corpus of scientificarticle metadata to obtain two subsets associated with the agent and theoutcome, including a first dataset associated with a first year, and asecond dataset associated with a second year; displaying the generalcausation visualization on the display device; displaying, in a portionof the general causation visualization associated with the first year, arepresentation of a first causation score computed by: determining arespective magnetism score for each respective article in the firstdataset based on directionality data, the directionality data indicatingwhether the respective article supports or rejects a hypothesis that theagent causes the outcome, and evidence data, the evidence dataindicating how well methodology of the article can demonstrate a causalrelationship between the agent and the outcome, aggregating therespective magnetism scores for the articles in the first dataset toobtain a magnetism score for the first dataset, weighting the magnetismscore based on proximity data indicating directness of evidence in eacharticle in the first dataset, and computing the first causation scorebased on the weighted magnetism score; and displaying, in a portion ofthe general causation visualization associated with the second year, arepresentation of a second causation score computed based on the seconddataset associated with the second year.
 2. The method of claim 1,wherein the first dataset further includes magnitude data, the magnitudedata indicating strength of association between the agent and theoutcome as observed in an associated article, the method furthercomprising: determining a magnitude score based on the magnitude data,wherein the first causation score is further based on the magnitudescore.
 3. The method of claim 2, wherein the magnitude data includes anodds ratio, and determining the magnitude score includes multiplying theodds ratio by another value.
 4. The method of claim 1, wherein the firstdataset further includes literature impact data, and the magnetism scoreis further based on the literature impact data.
 5. The method of claim4, wherein the literature impact data is determined independent of theagent and the outcome.
 6. The method of claim 1, further comprising:determining a coherence score based on the directionality data and theproximity data, wherein the first causation score is further based onthe coherence score.
 7. The method of claim 6, the method furthercomprising: calculating a test statistic of aggregated proximitycategorizations for articles in the first dataset, wherein the coherencescore is determined based on the calculated test statistic.
 8. Themethod of claim 1, wherein aggregating the respective magnetism scoresfor the articles in the first dataset includes aggregating the productof respective evidence data and respective directionality data for eachrespective article in the first dataset.
 9. The method of claim 1,wherein the evidence data includes a categorization of a methodology ofthe respective article, the method further comprising: selecting anevidence data value associated with the categorization.
 10. The methodof claim 1, wherein respective proximity categorizations categorize eacharticle in the first dataset as at least one of a human study, an animalstudy, and an in vitro study.
 11. The method of claim 1, wherein thegeneral causation visualization includes a graph of causation score as afunction of time, the graph including the representation of the firstcausation score and the representation of the second causation score.12. The method of claim 1, wherein processing the corpus of scientificarticle metadata includes: obtaining the first dataset including articlemetadata within a time threshold of the first year; and obtaining thesecond dataset including article metadata within the time threshold ofthe second year.
 13. A non-transitory computer readable storage mediumstoring instructions executable to perform a method of displaying, on adisplay device, a general causation visualization for an agent and anoutcome, the method comprising: processing a corpus of scientificarticle metadata to obtain two subsets associated with the agent and theoutcome, including a first dataset associated with a first year, and asecond dataset associated with a second year; displaying the generalcausation visualization on the display device; displaying, in a portionof the general causation visualization associated with the first year, arepresentation of a first causation score computed by: determining arespective magnetism score for each respective article in the firstdataset based on directionality data, the directionality data indicatingwhether the respective article supports or rejects a hypothesis that theagent causes the outcome, and evidence data, the evidence dataindicating how well methodology of the article can demonstrate a causalrelationship between the agent and the outcome, aggregating therespective magnetism scores for the articles in the first dataset toobtain a magnetism score for the first dataset, weighting the magnetismscore based on proximity data indicating directness of evidence in eacharticle in the first dataset, and computing the first causation scorebased on the weighted magnetism score; and displaying, in a portion ofthe general causation visualization associated with the second year, arepresentation of a second causation score computed based on the seconddataset associated with the second year.
 14. The non-transitory computerreadable storage medium of claim 13, wherein the first dataset furtherincludes magnitude data, the magnitude data indicating strength ofassociation between the agent and the outcome as observed in anassociated article, the method further comprising: determining amagnitude score based on the magnitude data, wherein the first causationscore is further based on the magnitude score.
 15. The non-transitorycomputer readable storage medium of claim 14, wherein the magnitude dataincludes an odds ratio, and determining the magnitude score includesmultiplying the odds ratio by another value.
 16. The non-transitorycomputer readable storage medium of claim 13, wherein the first datasetfurther includes literature impact data, and the magnetism score isfurther based on the literature impact data.
 17. The non-transitorycomputer readable storage medium of claim 16, wherein the literatureimpact data is determined independent of the agent and the outcome. 18.The non-transitory computer readable storage medium of claim 13, themethod further comprising: determining a coherence score based on thedirectionality data and the proximity data, wherein the first causationscore is further based on the coherence score.
 19. The non-transitorycomputer readable storage medium of claim 18, the method furthercomprising: calculating a test statistic of aggregated proximitycategorizations for articles in the first dataset, wherein the coherencescore is determined based on the calculated test statistic.
 20. Thenon-transitory computer readable storage medium of claim 13, whereinaggregating the respective magnetism scores for the articles in thefirst dataset includes aggregating the product of respective evidencedata and respective directionality data for each respective article inthe first dataset.